Abstract
This paper presents a method of classification rule discovery based on two multiple objective metaheuristics: a Greedy Randomized Adaptive Search Procedure with path-relinking (GRASP-PR), and Multiple Objective Particle Swarm (MOPS). The rules are selected at the creation rule process following Pareto dominance concepts and forming unordered classifiers. We compare our results with other well known rule induction algorithms using the area under the ROC curve. The multi-objective metaheuristic algorithms results are comparable to the best known techniques. We are working on different parallel schemes to handle large databases, these aspects will be subject of future works.
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Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA — a platform and programming language independent interface for search algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)
Clark, P., Niblett, T.: Rule induction with cn2: Some recent improvements. In: ECML: European Conference on Machine Learning, Springer, Berlin (1991)
Coello, C., Lechuga, M.: MOPSO: A proposal for multiple objective particle swarm optimization. In: IEEE World Congress on Computational Intelligence, pp. 1051–1056. IEEE Press, Los Alamitos (2002)
de Almeida Prado, A., Toracio, G., Pozo, A.T.R.: Multiple objective particle swarm for classification-rule discovery. In: 2007 IEEE Congress on Evolutionary Computation, September 25-28, 2007, pp. 684–691. IEEE Press, Los Alamitos (2007)
de la Iglesia, B., Philpott, M.S., Bagnall, A.J., Rayward-Smith, V.J.: Data mining rules using multi-objective evolutionary algorithms. In: Congress on Evolutionary Computation, pp. 1552–1559. IEEE Computer Society, Los Alamitos (2003)
de la Iglesia, B., Reynolds, A., Rayward-Smith, V.J.: Developments on a multi-objective metaheuristic (momh) algorithm for finding interesting sets of classification rules. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 826–840. Springer, Heidelberg (2005)
Fawcett, T.: Using rule sets to maximize roc performance. In: ICDM, pp. 131–138. IEEE Computer Society, Los Alamitos (2001)
Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109–133 (1995)
Tabu search and adaptive memory programming - advances, applications and challenges. In: Glover, F., Barr, R.S., Helgason, R.V., Kennington, J.L. (eds.) Interfaces in Computer Science and Operations Research, pp. 1–75. kluwer, Dordrecht (1996)
Ishibuchi, H.: Multiobjective association rule mining. In: PPSN Workshop on Multiobjective Problem Solving from Nature, pp. 39–48, Reykjavik, Iceland (2006)
Ishibuchi, H., Nojima, Y.: Accuracy-complexity tradeoff analysis by multiobjective rule selection. In: ICDM, pp. 39–48. IEEE Computer Society, Los Alamitos (2005)
Jin, Y.: Multi-Objective Machine Learning. Springer, Berlin, Boston, MA (2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1492–1948. IEEE Press, Los Alamitos (1995)
Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich (July 2005)
Laguna, M., Marti, R.: Grasp and path relinking for 2-layer straight line crossing minimization. INFORMS J. on Computing 11(1), 44–52 (1999)
Lavrac, N., Flach, P., Zupan, B.: Rule evaluation measures: A unifying view. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 174–185. Springer, Heidelberg (1999)
Prati, R.C., Flach, P.A.: ROCCER: An algorithm for rule learning based on ROC analysis. In: Kaelbling, L.P., Saffiotti, A. (eds.) IJCAI, pp. 823–828, Professional Book Center (2005)
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42(3), 203 (2001)
Resende, M., Ribeiro, C.: Greedy randomized adaptive search procedures. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 219–249. Kluwer Academic Publishers, Dordrecht (2002)
Shapiro, S.S., Wilk, M.B.: An analysis of variance test for normality (complete samples). Biometrika 52(3-4), 591–611 (1965)
Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)
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Ishida, C.Y., de Carvalho, A.B., Pozo, A.T.R., Goldbarg, E.F.G., Goldbarg, M.C. (2008). Exploring Multi-objective PSO and GRASP-PR for Rule Induction. In: van Hemert, J., Cotta, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2008. Lecture Notes in Computer Science, vol 4972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78604-7_7
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DOI: https://doi.org/10.1007/978-3-540-78604-7_7
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